Optimality of classification of random observations differing in their regression equations

The problem of classifying multidimensional observations different in the regression equations is considered for the case when the parameters of the regression equations are not available in advance and are estimated from a random sample (classified or not). Probability error estimates are provided for decision rules with learning and selflearning, depending on the sample size and the experiment design. A numerical example is given.